Treffer: End-to-End Statistical Model Checking for Parameterization and Stability Analysis of ODE Models ; Verification Statistiques de Modèles pour la Paramétrisation et l'Analyse de Stabilité de modèles différentiels

Title:
End-to-End Statistical Model Checking for Parameterization and Stability Analysis of ODE Models ; Verification Statistiques de Modèles pour la Paramétrisation et l'Analyse de Stabilité de modèles différentiels
Contributors:
Vérification pour l'Environnement et le LOgiciel (LS2N - équipe VELO), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-NANTES UNIVERSITÉ - École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), ANR-22-CE48-0012,BisoUS,Better Synthesis for Underspecified Quantitative Systems(2022)
Source:
ISSN: 1049-3301 ; ACM Transactions on Modeling and Computer Simulation ; https://hal.science/hal-04478120 ; ACM Transactions on Modeling and Computer Simulation, 2024, ⟨10.1145/3649438⟩.
Publisher Information:
CCSD
Association for Computing Machinery
Publication Year:
2024
Document Type:
Fachzeitschrift article in journal/newspaper
Language:
English
DOI:
10.1145/3649438
Rights:
http://hal.archives-ouvertes.fr/licences/copyright/ ; info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.5EE173CC
Database:
BASE

Weitere Informationen

International audience ; We propose a simulation-based technique for the parameterization and the stability analysis of parametric Ordinary Differential Equations. This technique is an adaptation of Statistical Model Checking, often used to verify the validity of biological models, to the setting of Ordinary Differential Equations systems. The aim of our technique is to estimate the probability of satisfying a given property under the variability of the parameter or initial condition of the ODE, with any metrics of choice. To do so, we discretize the values space and use statistical model checking to evaluate each individual value w.r.t. provided data. Contrary to other existing methods, we provide statistical guarantees regarding our results that take into account the unavoidable approximation errors introduced through the numerical integration of the ODE system performed while simulating. In order to show the potential of our technique, we present its application to two case studies taken from the literature, one relative to the growth of a jellyfish population, and the other concerning a well-known oscillator model.